{"url":"/dataset/af-classification-from-a-short-single-lead","name":"AF Classification from a Short Single Lead ECG Recording - The PhysioNet Computing in Cardiology Challenge 2017","full_name":null,"description_markdown":"The 2017 PhysioNet/CinC Challenge aims to encourage the development of algorithms to classify, from a single short ECG lead recording (between 30 s and 60 s in length), whether the recording shows normal sinus rhythm, atrial fibrillation (AF), an alternative rhythm, or is too noisy to be classified.","description_withheld":null,"homepage":"https://physionet.org/content/challenge-2017/1.0.0/","introduced_date":"2017-02-01","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Biomedical","url":"/datasets/modality/biomedical"}],"tasks":[{"name":"Atrial Fibrillation Detection","url":"/task/atrial-fibrillation-detection","datasets_with_task":"/datasets/task/atrial-fibrillation-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["AF Classification from a Short Single Lead ECG Recording - The PhysioNet Computing in Cardiology Challenge 2017"],"data_loaders":[],"num_papers_in_archive":5,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}